A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Interactive rendering of large volume data sets
Proceedings of the conference on Visualization '02
Out-of-core tensor approximation of multi-dimensional matrices of visual data
ACM SIGGRAPH 2005 Papers
Compression Domain Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Real-time Volume Graphics
Full Body Virtual Autopsies using a State-of-the-art Volume Rendering Pipeline
IEEE Transactions on Visualization and Computer Graphics
Transform Coding for Hardware-accelerated Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Hierarchical Tensor Approximation of Multi-Dimensional Visual Data
IEEE Transactions on Visualization and Computer Graphics
The Visual Computer: International Journal of Computer Graphics
GigaVoxels: ray-guided streaming for efficient and detailed voxel rendering
Proceedings of the 2009 symposium on Interactive 3D graphics and games
Tensor Decompositions and Applications
SIAM Review
Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
K-clustered tensor approximation: A sparse multilinear model for real-time rendering
ACM Transactions on Graphics (TOG)
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Interactive visual analysis of large and complex volume datasets is an ongoing and challenging problem. We tackle this challenge in the context of state-of-the-art out-of-core multiresolution volume rendering by introducing a novel hierarchical tensor approximation (TA) volume visualization approach. The TA framework allows us (a) to use a rank-truncated basis for compact volume representation, (b) to visualize features at multiple scales, and (c) to visualize the data at multiple resolutions. In this paper, we exploit the special properties of the TA factor matrix bases and define a novel multiscale and multiresolution volume rendering hierarchy. Different from previous approaches, to represent one volume dataset we use but one set of global bases (TA factor matrices) to reconstruct at all resolution levels and feature scales. In particular, we propose a coupling of multiscalable feature visualization and multiresolution DVR through the properties of global TA bases. We demonstrate our novel TA multiresolution hierarchy based volume representation and visualization on a number of μCT volume datasets.